Current research in FL has focused on image or text-based tasks. To our knowledge, the only known research or practical applications of FL in the audio domain are related to keyword spotting [1]}, [2]}, [3]}. Due to the limited research on FL for AED, there remain questions about the effectiveness of the approach due to varying acoustic conditions inherent to distributed clients. For example, data may be captured from clients in multiple locations with different background noise characteristics or in locations with only a subset of the sound event classes. This leads to differences in data distributions amongst the clients involved in the training process. For centralized training, data from multiple devices is combined into single training dataset that is typically assumed to be IID, but with FL distributed data collection leads to models being trained using data from divergent distributions, i.e., data that is non-IID. Existing AED datasets do not capture the non-IID characteristics seen with FL. To address this gap and foster research on FL for AED, we contribute novel AED datasets specifically designed for FL training.
Additionally, we provide baseline results for three neural network architectures to evaluate the effects of FL hyperparameters and non-IID data on AED performance.
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